This repository provides a suite of Google Colab notebooks for generating real-time and offline deepfake avatars from webcam feeds and videos. It targets users interested in creative AI applications, offering accessible interfaces for advanced models like First-Order Motion Model (FOMM), Wav2Lip, and Liquid Warping GAN without requiring local hardware or software installation.

How It Works

The project leverages Google Colab's GPU resources to run pre-trained deep learning models for animation and lip-syncing. It integrates webcam input via WebSockets and provides a GUI for parameter control, avatar swapping, and video processing. The core approach focuses on making complex models like FOMM (for head animation) and Wav2Lip (for lip-sync) easily accessible and usable in a browser-based environment.

Quick Start & Requirements

• Install/Run: Open provided Google Colab links (e.g., j.mp/cam2head).
• Prerequisites: Google account, webcam, internet connection. GPU access is provided by Colab.
• Setup: Minimal, as it's browser-based.
• Links: Colab for live avatars, YouTube/video avatars, Wav2Lip.

Highlighted Details

• Fastest purely online solution for live FOMM avatars.
• Real-time auto-calibration and exaggeration features.
• Drag-and-drop GUI for local/web image uploads.
• Supports various AI-generated faces (ThisPersonDoesNotExist, ThisWaifuDoesNotExist, etc.).
• Options for full-body deepfakes and green screen effects.

Maintenance & Community

The project is associated with numerous workshops and talks, indicating active engagement in the creative AI community. Specific contributor or sponsorship details are not prominent in the README.

Licensing & Compatibility

The README lists multiple underlying projects with varying licenses (e.g., MIT, Apache 2.0). The specific licensing for the avatars4all wrapper itself is not explicitly stated, requiring user diligence for commercial or closed-source integration.

Limitations & Caveats

As a Google Colab wrapper, performance and availability are dependent on Colab's infrastructure and usage limits. The project aggregates multiple external models, and compatibility or functionality may vary between them.